Method and system for scheduling multi-access edge computing resources

ABSTRACT

Systems and methods described herein provide an intelligent MEC resource scheduling service. A network device in a MEC network stores, in a memory, threshold values indicating overload conditions for resource usage by a first MEC cluster; monitors resource usage in the first MEC cluster; determines, based on the monitoring, when one of the threshold values is reached; identifies available resources in a second MEC cluster; and re-directs, based on the identifying, at least some of the resource usage from the first MEC cluster to the second MEC cluster.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.16/285,792, filed on Feb. 26, 2019, and titled “Method and System forScheduling Multi-Access Edge Computing Resources,” the contents of whichare incorporated herein by reference.

BACKGROUND

Development and design of radio access networks (RANs) presentchallenges from a network-side perspective and an end deviceperspective. To enhance performance, Multi-access Edge Computing (MEC)(also known as mobile edge computing) is being explored. In MEC, corenetwork capabilities (e.g., computational, storage, etc.) are situatedat the network edge to improve latency and reduce traffic. In contrastwith core network workloads, for example, MEC workloads can vary moresignificantly at each MEC location. Local cycles of human activity,unscheduled events, planned activities, local emergencies, and the likethat may have nominal impact on large scale network services (e.g.,nationally or regionally-based services) can reflect more heavily onlocal MEC workloads.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an exemplary environment in which an exemplaryembodiment of a MEC prediction service may be implemented;

FIG. 2 is a diagram of exemplary network connections in a portion of theenvironment of FIG. 1;

FIG. 3 illustrates exemplary logical components of the smart resourcescheduler of FIGS. 1 and 2;

FIG. 4 is a diagram of exemplary network connections in another portionof the environment of FIG. 1;

FIG. 5 illustrates exemplary components of a device that may correspondto one or more of the devices illustrated and described herein;

FIG. 6 is a flow diagram illustrating an exemplary process forscheduling MEC resources in the MEC network of FIG. 1;

FIG. 7 is a diagram of exemplary communications for selecting resourcesin remote MEC areas of the MEC network of FIG. 1;

FIG. 8 is a flow diagram illustrating an exemplary process for handlingabnormal workloads in an MEC area of the MEC network of FIG. 1; and

FIG. 9 is a flow diagram illustrating an exemplary process for emergencyhandling in an MEC area of the MEC network of FIG. 1.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following detailed description refers to the accompanying drawings.The same reference numbers in different drawings may identify the sameor similar elements. Also, the following detailed description does notlimit the invention.

New broadband cellular networks offer new features and benefits, such ashigh bandwidth, low latency, and support for massive Internet of Things(IoT) data transfers. One enhancement made possible through these newnetworks is the use of Multi-access Edge Computing (MEC) servers. Theseedge servers allow high network computing loads to be transferred ontothe edge servers. Depending on the location of the edge servers relativeto the point of attachment (e.g., a wireless station for an end device),MEC servers may provide various services and applications to end deviceswith minimal latency. Generally, lower latencies are achieved when MECservers are positioned with shorter distances to a network edge. Thus,service providers will need to establish MEC resources in multiplegeographic regions to minimize latency for services to mobile devicesand guarantee certain quality-of-service (QoS) levels. Due to the cyclesof human activity and other factors, workloads in some MEC service areasat any point in time may be lighter or heavier than workloads in otherMEC service areas.

According to implementations described herein, an intelligent MECresource scheduling service is provided. MEC resources are intelligentlyshared to maximize availability to meet the latency/bandwidthrequirements for different types of customer traffic. MEC resources inmore lightly-loaded MEC areas can be used to serveless-latency-sensitive workloads from the heavily-loaded MEC areas. Ifend device demands increase for a MEC area and exceed a configuredthreshold for the total resource capacity of the MEC area servicing theend devices, then end devices with lower priority (e.g., from anapplication latency aspect) can be offloaded to a neighboring MEC area.Examples of such resources may be a central processing unit (CPU),graphics processing unit (GPU), memory, bandwidth, etc. As describedfurther herein, distributed intelligence is provided in the resourcescheduling, so that computing resources across all available MEC areascan be used efficiently.

FIG. 1 illustrates an exemplary environment 100 in which an embodimentof the intelligent MEC resource scheduling service may be implemented.As illustrated, environment 100 includes an access network 105, a MECnetwork 130, a core network 150, and an external network 160. Accessnetwork 105 may include wireless stations 110-1 through 110-X (referredto collectively as wireless stations 110 and generally as wirelessstation 110). MEC network 130 may include MEC clusters 135 with smartresource schedulers (SRS) 140; core network 150 may include networkdevices 155; and external network 160 may include network devices 165.Environment 100 further includes one or more end devices 180.

The number, the type, and the arrangement of network device and thenumber of end devices 180 are exemplary. A network device, a networkelement, or a network function (referred to herein simply as a networkdevice) may be implemented according to one or multiple networkarchitectures, such as a client device, a server device, a peer device,a proxy device, a cloud device, a virtualized function, and/or anothertype of network architecture (e.g., Software Defined Networking (SDN),virtual, logical, network slicing, etc.). Additionally, a network devicemay be implemented according to various computing architectures, such ascentralized, distributed, cloud (e.g., elastic, public, private, etc.),edge, fog, and/or another type of computing architecture.

Environment 100 includes communication links between the networks,between the network devices, and between end devices 180 and the networkdevices. Environment 100 may be implemented to include wired, optical,and/or wireless communication links among the network devices and thenetworks illustrated. A connection via a communication link may bedirect or indirect. For example, an indirect connection may involve anintermediary device and/or an intermediary network not illustrated inFIG. 1. A direct connection may not involve an intermediary deviceand/or an intermediary network. The number and the arrangement ofcommunication links illustrated in environment 100 are exemplary.

Access network 105 may include one or multiple networks of one ormultiple types and technologies. For example, access network 105 mayinclude a Fourth Generation (4G) RAN, a 4.5G RAN, a 5G RAN, and/oranother type of future generation RAN. By way of further example, accessnetwork 105 may be implemented to include an Evolved UMTS TerrestrialRadio Access Network (E-UTRAN) of a Long Term Evolution (LTE) network,an LTE-Advanced (LTE-A) network, and/or an LTE-A Pro network, a nextgeneration (NG) RAN, and/or another type of RAN (e.g., a legacy RAN).Access network 105 may further include other types of wireless networks,such as a WiFi network, a Worldwide Interoperability for MicrowaveAccess (WiMAX) network, a local area network (LAN), or another type ofnetwork that may provide an on-ramp to wireless stations 110 and/or corenetwork 150.

Depending on the implementation, access network 105 may include one ormultiple types of wireless stations 110. For example, wireless station110 may include a next generation Node B (gNB), an evolved Node B (eNB),an evolved Long Term Evolution (eLTE) eNB, a radio network controller(RNC), a remote radio head (RRH), a baseband unit (BBU), a small cellnode (e.g., a picocell device, a femtocell device, a microcell device, ahome eNB, a repeater, etc.), or another type of wireless node. Wirelessstations 110 may connect to MEC network 130 via backhaul links 120, suchas wired or optical links. According to various embodiments, accessnetwork 105 may be implemented according to various architectures ofwireless service, such as, for example, macrocell, microcell, femtocell,or other configuration. Additionally, according to various exemplaryembodiments, access network 105 may be implemented according to variouswireless technologies (e.g., radio access technology (RAT), etc.),wireless standards, wireless frequencies/bands, and so forth.

MEC network 130 includes a platform that provides services at the edgeof a network, such as access network 105. MEC network 130 may beimplemented using one or multiple technologies including, for example,network function virtualization (NFV), software defined networking(SDN), cloud computing, or another type of network technology. Dependingon the implementation, MEC network 130 may include, for example,virtualized network functions (VNFs), multi-access (MA)applications/services, and/or servers. MEC network 130 may also includeother network devices that support its operation, such as, for example,a network function virtualization orchestrator (NFVO), a virtualizedinfrastructure manager (VIM), an operations support system (OSS), alocal domain name server (DNS), a virtual network function manager(VNFM), and/or other types of network devices and/or network resources(e.g., storage devices, communication links, etc.).

For purposes of illustration and description, MEC clusters 135 (alsoreferred to therein as “MEC areas”) include the various types of networkdevices that may be resident in MEC network 130. MEC clusters 135 may belocated to provide geographic proximity to various groups of wirelessstations 110. Some MEC clusters 135 may be co-located with networkdevices 155 of core network 150. According to some exemplaryembodiments, each MEC cluster 135 may include a local SRS 140.

SRS 140 may include logic that provides the intelligent MEC resourcescheduling services described herein. Each SRS 140 may monitor andpredict resource usage at a local MEC cluster 135. Each SRS 140 in eachlocal MEC cluster 135 may send its own status information and receivestatus information of other MEC clusters 135. SRS 140 may set prioritiesfor the types of workloads and set thresholds for resource usage in thelocal MEC cluster 135. As described further herein, the priorities ofthe workloads are defined according to the requirements of the latencyand performance of the workloads. According to an exemplary embodiment,SRS 140 may be included as logic within each of MEC clusters 135.According to another implementation, one or more functions of SRS 140described herein may be included in a centralized global orchestrationsystem for MEC network 130.

Each SRS 140 may attempt to use the resources from the local MEC cluster135 for workloads with high priorities. In one implementation, eachlocal MEC cluster 135 includes dedicated resources for high priorityworkloads, allocated for predicted workloads at each MEC cluster 135.When the usage of resources at the local MEC cluster 135 reaches apre-defined threshold, indicating a potential impact to the end userexperiences, SRS 140 will try to offload low priority workloads/trafficto the other lightly-loaded MEC clusters 135 for services.

Each local SRS 140 may communicate periodically with the other SRSs 140about its status. When a local MEC cluster 135 is lightly-loaded, theSRS 140 may notify SRSs 140 in other MEC clusters 135, indicating thetypes of resources available to contribute, the quantity of resourcesavailable to contribute, and estimation of how long the MEC cluster 135can contribute.

According to another implementation, SRS 140 may also broadcast anemergency request to the other SRSs 140 for additional resources tohandle a high workload. When the emergency request is received, otherSRSs 140 may share their status information with the requesting SRS 140,indicating resources currently available and a forecast of resourceusage (e.g., for the next few hours). The requesting SRS 140 may selectthe MEC cluster(s) 135 to server/share the emergency workload until thelevel of traffic is reduced below a threshold value at the local MECcluster 135.

Core network 150 may include one or multiple networks of one or multiplenetwork types and technologies to support access network 105. Forexample, core network 150 may be implemented to include a nextgeneration core (NGC) network for a 5G network, an Evolved Packet Core(EPC) of an LTE network, an LTE-A network, an LTE-A Pro network, and/ora legacy core network. Depending on the implementation, core network 150may include various network devices 155, such as for example, a userplane function (UPF), an access and mobility management function (AMF),a session management function (SMF), a unified data management (UDM)device, an authentication server function (AUSF), a network sliceselection function (NSSF), a network repository function (NRF), a policycontrol function (PCF), and so forth. According to other exemplaryimplementations, core network 150 may include additional, different,and/or fewer network devices than those described. For purposes ofillustration and description, network devices 155 may include varioustypes of network devices that may be resident in core network 150, asdescribed herein.

External network 160 may include one or multiple networks. For example,external network 160 may be implemented to include a service or anapplication-layer network, the Internet, an Internet Protocol MultimediaSubsystem (IMS) network, a Rich Communication Service (RCS) network, acloud network, a packet-switched network, or other type of network thathosts an end device application or service. For example, the end deviceapplication/service network may provide various applications or servicespertaining to broadband access in dense areas (e.g., pervasive video,smart office, operator cloud services, video/photo sharing, etc.),broadband access everywhere (e.g., 50/100 Mbps, ultralow-cost network,etc.), higher user mobility (e.g., high speed train, remote computing,moving hot spots, etc.), Internet of Things (IoTs) (e.g., smartwearables, sensors, mobile video surveillance, etc.), extreme real-timecommunications (e.g., tactile Internet, etc.), lifeline communications(e.g., natural disaster, etc.), ultra-reliable communications (e.g.,automated traffic control and driving, collaborative robots,health-related services (e.g., monitoring, remote surgery, etc.), dronedelivery, public safety, etc.), and/or broadcast-like services.

Depending on the implementation, external network 160 may includevarious network devices 165 that provide various applications, services,or other type of end device assets, such as servers (e.g., web,application, cloud, etc.), mass storage devices, data center devices,and/or other types of network devices pertaining to variousnetwork-related functions. According to an exemplary embodiment, one ormultiple network devices 165 may also be included within MEC clusters135 to support the intelligent MEC resource scheduling service, asdescribed herein.

End device 180 includes a device that has computational and wirelesscommunication capabilities. End device 180 may be implemented as amobile device, a portable device, a stationary device, a device operatedby a user, or a device not operated by a user. For example, end device180 may be implemented as a Mobile Broadband device, a smartphone, acomputer, a tablet, a netbook, a wearable device, a vehicle supportsystem, a game system, a drone, or some other type of wireless device.According to various exemplary embodiments, end device 180 may beconfigured to execute various types of software (e.g., applications,programs, etc.). End device 180 may support one or multiple RATs (e.g.,4G, 5G, etc.), one or multiple frequency bands, network slicing,dual-connectivity, and so forth. Additionally, end device 180 mayinclude one or multiple communication interfaces that provide one ormultiple (e.g., simultaneous or non-simultaneous) connections via thesame or different RATs, frequency bands, etc.

FIG. 2 is a diagram of exemplary network connections in a networkportion 200 of environment 100. As shown in FIG. 2, network portion 200may include multiple MEC areas 210-1 through MEC area 210-4. Each MECarea 210 may include a MEC cluster 135 with an SRS 140 and multiplewireless stations 110 servicing end devices 180. Each MEC cluster 135may be connected within MEC network 130. Each MEC cluster 135 may becommunicatively coupled to one or more wireless station 110 (e.g., viawired or optical links 120). End devices 180 may have a wirelessconnection with wireless station 110 via one or more RAT types. The MECcluster 135 and/or SRS 140 in each MEC area 210 is connected to the MECclusters 135 and/or SRSs 140 in each of other MEC areas 210 by wiredconnections 220 (e.g. optical fibers).

In the arrangement of network portion 200, the transport/link betweenany two MEC areas 210 is optimized and the latency between any two MECareas 210 is minimized. The SRSs 140 in connected MEC areas 210 maysubscribe with each other to share information of local MEC cluster 135resource usages, forecasts, and availabilities.

While FIG. 2 shown four MEC areas 210 for simplicity, in practice dozensor hundreds of MEC areas 210 may be located on a national or regionalscale for a particular service provider.

FIG. 3 is a diagram illustrating exemplary components of smart resourcescheduler (SRS) 140. In one implementation, all or some of thecomponents illustrated in FIG. 3 may be implemented by a processor (e.g.processor 410 described below in connection with FIG. 4) executingsoftware instructions stored in a memory (e.g., memory/storage 415). Asshown in FIG. 3, SRS 140 may include a local MEC Resource usage monitor310, an inter-SRS communications manager 320, a local MEC resource usageforecaster 330, an anomaly/emergency handler 340, and a local MECworkload and traffic manager 350.

Local MEC resource usage monitor 310 may monitor the resource usage of arespective local MEC cluster 135 (e.g., in MEC area 210-1 or 210-2,etc.). Resource usage may include, for example, such as CPU/GPU usage,RAM usage, storage usage, bandwidth usage, etc., by MEC cluster 135.Resource usage may be monitored and reported to other MEC areas 210(e.g., respective SRSs 140).

Inter-SRS communications manager 320 may communicate with other MECclusters 135 about, for example, MEC resource usages and availability ofMEC resources. Resource usage information about a local MEC cluster 135may be shared with the SRSs 140 in other MEC areas 210. Additionally,inter-SRS communications manager 320 may exchange messages with remoteMEC clusters 135 to facilitate workload handovers.

Local MEC resource usage forecaster 330 may be used to predict/forecastMEC resource usages in the future, such as a period of the next fewhours, days, etc., for an individual MEC cluster 135. Local MEC resourceusage forecaster 330 may provide forecasts for CPU usage, RAM usage,storage usage, bandwidth usage, etc. According to one implementation,Local MEC resource usage forecaster 330 may distribute forecasts toother MEC areas 210 (e.g., respective SRSs 140) through periodicmessages or responses to requests via inter-SRS communications manager320.

According to one implementation, local MEC resource usage forecaster 330may locally apply artificial intelligence (AI), such as machine learning(ML) or deep learning (DL), to forecast MEC resource usage. Thealgorithms and models used in AI may be the same at each local MECresource usage forecaster 330. Data used in AI may be shared across MECareas 210. In another implementation, AI computations may performedusing be shared MEC resources. For example, computations may be done atMEC areas 210 with light workloads, with results sent to interestedparties or a requester. AI computations may be used for analytics onresource usage at one MEC area 210, on an individual applicationinstance, on a service from one 5G MEC area.

Abnormality/emergency handler 340 may be used to detect a localabnormality of resource usage and/or emergency situations in a local MECcluster 135. When the resource usages of a local MEC cluster 135 reach apre-defined criteria value, abnormality/emergency handler 340 may treatthe situation as an abnormality or emergency. In one implementation,abnormality/emergency handler 340 may rely on predictive forecasts fromlocal MEC resource usage forecaster 330 to detect an abnormality. Anabnormality may include, for example, above- or below-normal resourceuse by a particular application or user that may not lead to an imminentservice disruption. An emergency, in contrast, may include, for example,a network disruption or physical problem that impacts delivery orservices.

Local MEC workload and traffic manager 350 may consume analytics datafrom local MEC resource usage monitor 310, local MEC resource usageforecaster 330, and abnormality/emergency handler 340, as well as datareceived from other MEC clusters 135 via inter-SRS communicationsmanager 320. Based on the analytics data, local MEC workload and trafficmanager 350 may make recommendations on whether the traffic could bebetter routed to other available MEC resources (MEC clusters 135 inother MEC areas 210). For example, local MEC workload and trafficmanager 350 may store threshold values (e.g., as absolute values orpercentage levels of different resources) that may be compared withcurrent or forecasted analytics data to determine whether to off-loadsome traffic to remote MEC clusters.

According to an implementation, local MEC workload and traffic manager350 may store rules that designate priority levels for different typesof traffic. For example, in each MEC cluster 135, there may be dedicatedresources for workloads with different types of priorities. A thresholdvalue may be selected for each type of workload according to itspriority. Table 1 below illustrates different priorities and thresholdlevels that may be assigned for use by local MEC workload and trafficmanager 350 in determining whether local traffic needs to be off-loaded.

TABLE 1 Threshold of Local Resource Priority Usage Workload Example ofWorkload Extremely 80% Workload requiring Live VR, AR, smart highextreme high bandwidth, car, robotics extreme low latency Very high 80%Workload requiring very Cloud games, HD high bandwidth, very lowstreaming latency High 85% Workload requiring high HD Video call, smartbandwidth, low latency assistant Medium 90% Workload requiringTraditional streaming, medium bandwidth and video call latency Low 90%Workload using low Traditional web bandwidth, no requirement browsingfor latency Extremely 95% Workload using low Sending/receiving lowbandwidth, no requirement text messages for latency

When usage of the resources in local MEC area for the type of workloadreaches the threshold value (e.g., 80%, 85%, etc.), local MEC workloadand traffic manager 350 may pull in additional resources to handle theworkload, either from local temporary resources or remote MEC clusters135. The type of workload for incoming traffic/service request may bedetermined, for example, by a QoS indicator associated with the trafficflow. In one implementation, to avoid conflicts, each MEC cluster 135 inan MEC area 210 may apply the same rules as those used in other MECareas. Table 2 below illustrates general rules that may be applied bylocal MEC workload and traffic manager 350 for different workloadpriority types.

TABLE 2 Priority of Workload Rules for Selecting MEC Resources ExtremelyAlways look for local MEC resources first high If needed, always lookfor remote MEC resources with lowest latency to end devices Very highAlways look for local MEC resources first If needed, always look forremote MEC resources with lowest latency to end devices High Always lookfor local MEC resources first If needed, always look for remote MECresources with lowest latency to end devices Medium Look for local MECresources as well as remote MEC resources When possible, use local MECresources If needed, use remote MEC resources Low Look for local MECresources as well as remote MEC resources When possible, use remote MECresources Extremely Always look for remote MEC resources as well aslocal low resources When possible, use remote MEC resource first

The following use case illustrates the intelligent MEC resourcescheduling service. After initial deployment, 5G service carriers maysetup additional MEC resources to serve the increasing customer needs.For example, in New York City, a carrier can set up several MEC clusters135 in each district/borough. These MEC clusters 135 can be close toeach other and the round trip time of network traffic from nearby MECcluster resources can meet the requirements for latency and bandwidth.When one MEC cluster 135 is heavily loaded with customer traffic, someof the MEC resources in one MEC areas 210 may not meet thelatency/bandwidth requirements, but MEC resources from nearby MEC areas210 may meet the latency/bandwidth requirements. In this case, MECresources from nearby MEC clusters 135 can be treated as local resourcesand can be pulled in to serve customer traffic. In one implementation,if links between MEC clusters 135 (e.g., transport links 220) areoptimized, it may be assumed that an MEC cluster 135 (if not alsooverloaded) closest to the heavily loaded MEC cluster 135 will have thelowest latencies to end device 180 that were previously being servicesby the heavily loaded MEC cluster 135.

The above example may change when the nearby MEC cluster 135 alsobecomes heavily loaded by customer traffic. Thus, SRS 140 may constantlyshare the information of its local MEC cluster 135 with the SRS 140 inthe other MEC clusters 135.

Two factors in determining whether MEC resources can be treated as localinclude (1) if the MEC resources are close to the user, and (2) if theperformance of the MEC resources meets the latency/bandwidthrequirements. When determining whether the MEC resources are closeenough to the user, the SRS 140 may consider geophysical distance (e.g.,the distance between the MEC location and the user) and fiber lengths(e.g., whether the fiber length from the gNB 110 to the MEC cluster 135is smaller than the fiber length from the gNB 110 to the other MECclusters 135). Some MEC clusters 135 could be geophysical close to theuser, but the fiber length between the MEC cluster 135 and the gNB maynot be short enough. To determine whether the performance of the MECresources meets the latency/bandwidth requirements, SRS 140 mayconstantly monitor the performance of the MEC resources.

Although FIG. 3 shows exemplary logical components of SRS 140, in otherimplementations, SRS 140 may include fewer components, differentcomponents, or additional functional components than those depicted inFIG. 3. Additionally or alternatively, one or more functions of thecomponents of SRS 140 may be performed by, or assisted by, anothernetwork device in environment 100.

FIG. 4 is a diagram of exemplary network connections in a networkportion 400 of network environment 100. More particularly, networkportion 400 illustrates an implementation of the intelligent MECresource scheduling service in a 5G network environment. As shown inFIG. 4, network portion 400 may include access network 105 with wirelessstation 110, MEC cluster 135, core network 150, external network 160,and end device 180.

In the example of FIG. 4, access network 105 may include a 5G New Radionetwork. Wireless station 110 may include a gNB 110 having, for example,one or more distributed units (DU) 470 and a centralized unit (CU) 475.In some implementation, DU 470 and CU 475 may be co-located.

MEC cluster 135 may include a UPF 445, an MEC orchestrator 450, and alocal area data network (LADN) 455. Thus, in contrast with other 5Gembodiments, in the configuration of FIG. 4, the user plane function(e.g., UPF 445) is located within MEC cluster 135, instead of corenetwork 150.

UPF 445 may maintain an anchor point for intra/inter-RAT mobility,maintain an external Packet Data Unit (PDU) point of interconnection toa data network (e.g., DN 460), perform packet routing and forwarding,perform the user plane part of policy rule enforcement, perform packetinspection, perform traffic usage reporting, enforce QoS policies in theuser plane, perform uplink traffic verification, perform transport levelpacket marking, perform downlink packet buffering, send and forward an“end marker” to a Radio Access Network (RAN) node (e.g., gNB 110),and/or perform other types of user plane processes. UPF 445 maycommunicate with SMF 440 (e.g., using an N4 interface) and connect todata network 460 (e.g., using an N6 interface).

MEC orchestrator 450 may automate sequences of activities, tasks, rules,and policies needed for on-demand creation, modification, or removal ofnetwork, application, or infrastructure services and resources. MECorchestrator 450 may provide orchestration at a high level, with anend-to-end view of the infrastructure, network, and applications. In theconfiguration of FIG. 4, MEC orchestrator 450 may include an SRS 140 orsome logical components of SRS 140.

As shown in FIG. 4, core network 150 may include a Network DataAnalytics Function (NWDAF) 405, an NSSF 410, an AUSF 415, a UDM 420, aPCF 425, a network exposure function (NEF) 430, an AMF 435, and a SMF440. NWDAF 405, NSSF 410, AUSF 415, UDM 420, PCF 425, NEF 430, AMF 435,and SMF 440 may correspond, for example, to network devices 155 ofFIG. 1. In another implementation, multiple logical components may beexecuted on a single network device 155.

NWDAF 405 may include logic that analyzes congestion information (suchas MEC resource usage from MEC clusters 135). NWDAF 405 may includelogic that stores congestion threshold parameters and values, and mayuse these parameters and values for comparison to the parameters andvalues included in the congestion information received from MEC clusters135. The congestion threshold parameters and values may pertain to loadlevels in relation to various network resources (e.g., physical,logical, virtual) including, for example, CPU, GPU, and memory;bandwidth, etc. According to an exemplary embodiment, NWDAF 405 maydetermine whether congestion and/or predictive congestion exist(s) basedon a result of the comparison.

NSSF 410 may select a set of network slice instances to serve aparticular end device 180, determine a particular AMF 220 to serve aparticular end device 180, and/or perform other types of processesassociated with network slice selection or management. AUSF 415 maymanage permissions for end devices 180. For example, AUSF 415 may verifythat an end device 180 is authorized to access particular types ofnetwork services. UDM 420 may maintain subscription information for enddevices 180. For example, UDM 420 may create an authentication vector,manage user profiles, perform subscription management, conduct roamingauthorization, etc. PCF 425 may provide policy rules to control planefunctions (e.g., to SMF 240). NEF 430 may expose capabilities and eventsto other network functions, including third party NFs, applicationfunctions, edge computing network functions, and/or other types ofnetwork functions. AMF 435 may perform control plane functions such asregistration, authentication, paging, and bearer setup. SMF 440 mayprovide control plane functions, such as assigning an end device IPaddress, interfacing with QoS policy, and configuring a UPF (e.g., UPF445) for packet forwarding.

In the configuration of FIG. 4, SRS 140 is treated as a component of theMEC orchestrator 450 in the local MEC cluster 135.

Resources (e.g., CPU, GPU, RAM, storage, etc.) at each local MEC cluster135 may be virtualized. Applications and services at the local MECcluster 135 consume the virtualized resources. SRS 140 at the local MECcluster 135 monitors and forecasts resource usages at the local MEC 135.SRS 140 at the local MEC cluster 135 shares the resource usageinformation and resource availability with SRSs 140 at other MECclusters 135.

SRS 140 may communicate with NWDAF 405 to obtain network statusinformation. SRS 140 may also communicate with UPF 445 to obtainapplication-specific traffic information. SRS 140 may communicate withother 5G core network components, such as UDM 420, PCF 425, etc., toobtain other information about the user and service qualityrequirements. SRS 140 may recommend whether the user traffic should uselocal MEC cluster 135 resources or can be routed to other availableresources in other MEC clusters 135. When resources at local MEC cluster135 are available, these available resources can be used to serve userrequests from other MEC areas 210.

Although FIG. 4 shows an exemplary arrangement of components of networkportion 400, in other implementations, network portion 400 may includefewer components, different components, differently-arranged components,or additional components than depicted in FIG. 4. For example, inanother implementation, an NWDAF component may be included within localMEC clusters 135 to reduce the time for the SRS 140 to get informationfrom NWDAF 405 in the core network 150.

FIG. 5 is a diagram illustrating example components of a device 500according to an implementation described herein. Wireless station 110,MEC cluster 135, SRS 140, network device 155, network device 165, enddevice 180, and/or UPF 445, MEC orchestrator 450 may each include one ormore devices 500. In another implementation, a device 500 may includemultiple network functions. As illustrated in FIG. 5, according to anexemplary embodiment, device 500 includes a bus 505, a processor 510, amemory/storage 515 that stores software 520, a communication interface525, an input 530, and an output 535. According to other embodiments,device 500 may include fewer components, additional components,different components, and/or a different arrangement of components thanthose illustrated in FIG. 5 and described herein.

Bus 505 includes a path that permits communication among the componentsof device 500. For example, bus 505 may include a system bus, an addressbus, a data bus, and/or a control bus. Bus 505 may also include busdrivers, bus arbiters, bus interfaces, and/or clocks.

Processor 510 includes one or multiple processors, microprocessors, dataprocessors, co-processors, application specific integrated circuits(ASICs), controllers, programmable logic devices, chipsets,field-programmable gate arrays (FPGAs), application specificinstruction-set processors (ASIPs), system-on-chips (SoCs), centralprocessing units (CPUs) (e.g., one or multiple cores), microcontrollers,and/or some other type of component that interprets and/or executesinstructions and/or data. Processor 510 may be implemented as hardware(e.g., a microprocessor, etc.), a combination of hardware and software(e.g., a SoC, an ASIC, etc.), may include one or multiple memories(e.g., cache, etc.), etc. Processor 510 may be a dedicated component ora non-dedicated component (e.g., a shared resource).

Processor 510 may control the overall operation or a portion ofoperation(s) performed by device 500. Processor 510 may perform one ormultiple operations based on an operating system and/or variousapplications or computer programs (e.g., software 520). Processor 510may access instructions from memory/storage 515, from other componentsof device 500, and/or from a source external to device 500 (e.g., anetwork, another device, etc.). Processor 510 may perform an operationand/or a process based on various techniques including, for example,multithreading, parallel processing, pipelining, interleaving, etc.

Memory/storage 515 includes one or multiple memories and/or one ormultiple other types of storage mediums. For example, memory/storage 515may include one or multiple types of memories, such as, random accessmemory (RAM), dynamic random access memory (DRAM), cache, read onlymemory (ROM), a programmable read only memory (PROM), a static randomaccess memory (SRAM), a single in-line memory module (SIMM), a dualin-line memory module (DIMM), a flash memory (e.g., a NAND flash, a NORflash, etc.), and/or some other type of memory. Memory/storage 515 mayinclude a hard disk (e.g., a magnetic disk, an optical disk, amagneto-optic disk, a solid state disk, etc.), a Micro-ElectromechanicalSystem (MEMS)-based storage medium, and/or a nanotechnology-basedstorage medium. Memory/storage 515 may include a drive for reading fromand writing to the storage medium.

Memory/storage 515 may be external to and/or removable from device 500,such as, for example, a Universal Serial Bus (USB) memory stick, adongle, a hard disk, mass storage, off-line storage, network attachedstorage (NAS), or some other type of storing medium (e.g., a compactdisk (CD), a digital versatile disk (DVD), a Blu-Ray disk (BD), etc.).Memory/storage 515 may store data, software, and/or instructions relatedto the operation of device 500.

Software 520 includes an application or a program that provides afunction and/or a process. Software 520 may include an operating system.Software 520 is also intended to include firmware, middleware,microcode, hardware description language (HDL), and/or other forms ofinstruction. Additionally, for example, MEC cluster 135 and/or MECorchestrator 450 may include logic to perform tasks, as describedherein, based on software 520. Furthermore, end devices 180 may storeapplications that require services/resources from MEC clusters 135.

Communication interface 525 permits device 500 to communicate with otherdevices, networks, systems, devices, and/or the like. Communicationinterface 525 includes one or multiple wireless interfaces and/or wiredinterfaces. For example, communication interface 525 may include one ormultiple transmitters and receivers, or transceivers. Communicationinterface 525 may include one or more antennas. For example,communication interface 525 may include an array of antennas.Communication interface 525 may operate according to a communicationstandard and/or protocols. Communication interface 525 may includevarious processing logic or circuitry (e.g.,multiplexing/de-multiplexing, filtering, amplifying, converting, errorcorrection, etc.).

Input 530 permits an input into device 500. For example, input 530 mayinclude a keyboard, a mouse, a display, a button, a switch, an inputport, speech recognition logic, a biometric mechanism, a microphone, avisual and/or audio capturing device (e.g., a camera, etc.), and/or someother type of visual, auditory, tactile, etc., input component. Output535 permits an output from device 500. For example, output 535 mayinclude a speaker, a display, a light, an output port, and/or some othertype of visual, auditory, tactile, etc., output component. According tosome embodiments, input 530 and/or output 535 may be a device that isattachable to and removable from device 500.

Device 500 may perform a process and/or a function, as described herein,in response to processor 510 executing software 520 stored bymemory/storage 515. By way of example, instructions may be read intomemory/storage 515 from another memory/storage 515 (not shown) or readfrom another device (not shown) via communication interface 525. Theinstructions stored by memory/storage 515 cause processor 510 to performa process described herein. Alternatively, for example, according toother implementations, device 500 performs a process described hereinbased on the execution of hardware (processor 510, etc.).

FIG. 6 is a flow diagram illustrating an exemplary process 600 forscheduling MEC resources in MEC network 130. In one implementation,process 600 may be implemented by an SRS 140 in an MEC cluster 135. Inanother implementation, process 600 may be implemented by more than oneMEC clusters 135 in conjunction with one or more other devices innetwork environment 100.

As shown in FIG. 6, an MEC cluster may be registered as a local MECcluster for an MEC network (block 605) and may check for currentresource usage and/or predict resource usage (block 610). For example,SRS 140 may register a corresponding local MEC cluster 135 to providesubscription based updates with other MEC clusters in differentgeographic areas. SRS 140 (e.g., local MEC Resource usage monitor 310and/or local MEC resource usage forecaster 330) may monitor current MECresource use and predict future MEC resource use, such as CPU/GPU usage,RAM usage, storage usage, bandwidth usage, or other resources of MECcluster 135.

Process 600 may also include determining if a workload threshold hasbeen reached (block 615). For example, SRS 140 (e.g., local MEC workloadand traffic manager 350) may compare analytics data from local MECresource usage monitor 310 and/or local MEC resource usage forecaster330 with stored threshold values to determine whether it is necessary tooff-load some traffic to remote MEC clusters. In one implementation,local MEC workload and traffic manager 350 may compare an appropriatethreshold value from Table 1 with current/projected resource usagelevels for each of the dedicated resources in MEC cluster 135.

If a workload threshold has not been reached (block 615—No), process 600may include scheduling resources per rules (block 620), checkingresource usage and availability (block 625), and sharing information oflocal resource usage and/or availability (block 630). For example, SRS140 (e.g., local MEC workload and traffic manager 350) may assignpriority levels (e.g., “extremely high,” “very high,” “high,” etc.) toincoming workloads and apply rules based on workload priorities andresource usage levels, such as shown above in Tables 1 and 2. Local MECworkload and traffic manager 350 may also determine if any resourcesfrom MEC cluster 135 are available for sharing based on the current andprojected workloads. Inter-SRS communications manager 320 may report thecurrent and/or projected workload levels to the MEC clusters 135 inother MEC areas 210 for their consideration.

If a workload threshold has been reached (block 615—Yes), process 600may include checking available remote MEC resources (block 635), anddetermining if an available remote MEC resource meets QoS requirements(block 640). For example, when SRS 140 (e.g., local MEC workload andtraffic manager 350) determines that an overloaded condition exists oris pending, reports of current and/or projected workload levels at otherMEC clusters 135 (e.g., received periodically by inter-SRScommunications manager 320) may be reviewed for remote resourceavailability. Resources with availability may be reviewed/tested forability to meet QoS requirements for the type of workload that needs tobe transferred. QoS requirements may include, for example, availablebandwidth, latency, and/or length of available time from the potentialremote MEC cluster 135.

If an available remote MEC resource does not meet QoS requirements(block 640—No), process 600 may return to process block 635 to check forother available remote MEC resources. If an available remote MECresource meets QoS requirements (block 640—Yes), process 600 may includeselecting one or more remote MEC resources (block 645), and off-loadingworkload to selected remote resources (block 650). For example, localMEC workload and traffic manager 350 may apply priority workload rules(e.g., from Table 2 above), latency measurements, geographic distances,and physical link (e.g., link 220) length to select a best availableremote MEC cluster 135. Based on the selection criteria, local MECworkload and traffic manager 350 may communicate with end device 180and/or the selected MEC cluster 135 to facilitate a workload handover.

FIG. 7 is a diagram of exemplary communications for selecting resourcesin a portion 700 of network environment 100. Communications shown inFIG. 7 may correspond to process blocks 635 through 645 of FIG. 6.Network portion 700 may include access network 105 with wireless station110, MEC clusters 135-1 and 135-2 of MEC network 130, data network 160,and end device 180.

For FIG. 7, it is assumed that the transport link 220 between two MECclusters 135-1 and 135-2 is optimized, and that the latency between MECclusters 135-1 and 135-2 is minimized. Furthermore, assume a wirelessRAN connection 705 between end device 180 and one of distributed units470 and a wired (e.g., optical fiber) connection between eachdistributed unit 470 and centralized unit 475.

In the example of FIG. 7, end device 180 is initially connected to MECcluster 135 to receive services. SRS 140-1 may determine that both MECcluster 135-1 and MEC cluster 35-2 can each meet the user/servicerequirements for latency and bandwidth for “High” and lower priorityworkloads (see, e.g., Table 1). Assume only MEC cluster 135-1 can meetservice requirements for “Extremely High” and “Very High” priorityworkloads. MEC cluster 135-1 is closer to end device 180/wirelessstation 110 than MEC cluster 135-2. Thus, the priority would be to useresources in MEC cluster 135-1 (e.g., via link 120-1).

If the situation in MEC cluster 135-1 changes and resources in MECcluster 135-1 are heavily used, “High” (or lower) priority traffic fromend device 180 can be routed to MEC cluster 135-2 (e.g., via link120-2). Inter-SRS communications manager 320 may instruct UPF 445-1 tocommunicate with UPF 445-2 to transfer context data for the session(e.g., via link 220). However, “Extremely High” or “Very High” prioritytraffic from end device 180 would not be off-loaded from MEC cluster135-1 to MEC cluster 135-2. Instead, MEC cluster 135-1 may re-purposelocal resources to meet “Extremely High” or “Very High” prioritydemands.

FIG. 8 is a flow diagram illustrating an exemplary process 800 formanaging abnormalities in MEC network 130. In one implementation,process 800 may be implemented by an SRS 140 in an MEC cluster 135. Inanother implementation, process 800 may be implemented by more than oneMEC clusters 135 in conjunction with one or more other devices innetwork environment 100.

As shown in FIG. 8, an MEC cluster may be registered as a local MECcluster for an MEC network (block 805) and may check for currentresource usage and/or predict resource usage (block 810). For example,as described above in connection with FIG. 6, SRS 140 may register acorresponding local MEC cluster 135 to provide subscription basedupdates with other MEC clusters in different geographic areas and maymonitor MEC resource use.

Process 800 may also include determining if an abnormality is observed(block 815). For example, SRS 140 (e.g., abnormality/emergency handler340) monitor for a signal indicating an abnormality with MEC cluster135, which may include signals indicating, for example, change intypical use patterns, slow responses when workloads are low, etc.

If no abnormality is observed (block 815—No), process 800 may continueto monitor for abnormalities (block 820) and return to process block810. If an abnormality is observed (block 815—Yes), process 800 mayinclude examining application and account level information (block 825),monitoring the service behavior (block 830), and routing thetraffic/workload to designated resources (block 835). For example, SRS140 (e.g., abnormality/emergency handler 340) may retrieve from PCF 425user account information to confirm if user subscription settings (e.g.,bandwidth, throttling, etc.) support QoS requirements for a requestedservice. Abnormality/emergency handler 340 may monitor and recordsession behavior for a short interval (e.g., one minute) and theninstruct local MEC workload and traffic manager 350 to off-load theworkload for the particular session using procedures described above(e.g., transfer from MEC cluster 135-1 to MEC cluster 135-2).

Process 800 may further include waiting for troubleshooting and issueresolution (block 840) and returning traffic and workload to thepreferred MEC resources (block 845). For example, abnormality/emergencyhandler 340 may generate an alert signal for a network administrator toaddress the abnormality. In one implementation, the signal may include alink to the stored session behavior (e.g., recorded at block 825). Oncethe abnormality is resolved, he network administrator may update thestatus for abnormality/emergency handler 340 and local MEC workload andtraffic manager 350 may return the traffic and workload to the preferredMEC cluster (e.g., MEC cluster 135-1).

FIG. 9 is a flow diagram illustrating an exemplary process 900 formanaging emergencies in MEC network 130. In one implementation, process900 may be implemented by an SRS 140 in an MEC cluster 135. In anotherimplementation, process 900 may be implemented by more than one MECclusters 135 in conjunction with one or more other devices in networkenvironment 100. For handling emergency conditions, it may be assumedthat the functions and availability of SRS 140 in a local MEC area 210are not impacted by the emergency situation. In some implementations, toreduce the possibility that SRS 140 is impacted by an emergencysituation, SRS 140 in local MEC area 210 may have certain protections,such as separate battery backup, emergency communication links (e.g.,wired or wireless), separate partitions, etc.

As shown in FIG. 9, an MEC cluster may be registered as a local MECcluster for an MEC network (block 905) and may check for currentresource usage and/or predict resource usage (block 910). For example,as described above in connection with FIG. 6, SRS 140 may register acorresponding local MEC cluster 135 to provide subscription basedupdates with other MEC clusters in different geographic areas and maymonitor MEC resource use.

Process 900 may also include determining if an emergency is observed(block 915). For example, SRS 140 (e.g., abnormality/emergency handler340) may monitor for a signal indicating an emergency situation with MECcluster 135, which may indicate a loss of power, a loss of communication(e.g., a fiber cut), flooding, malicious attack, etc.

If no emergency is observed (block 915—No), process 900 may continue formonitoring for abnormalities (block 920) and return to process block910. If an emergency is observed (block 915—Yes), process 900 mayinclude collecting details and plotting strategy for handling theemergency situation (block 925), and allocating local MEC workloads to aclosest MEC area according to QoS requirements (block 930). For example,SRS 140 (e.g., abnormality/emergency handler 340) may collect dataregarding the scope/impact of the emergency and determine which (or all)local MEC resources are impacted. Abnormality/emergency handler 340 mayidentify other available MEC clusters to which workloads can beoffloaded. Abnormality/emergency handler 340 may map out assignments fordifferent workloads to available remote MEC clusters 135 based onpriority and QoS requirements.

Process 900 may further include routing the traffic/workload todesignated resources (block 935). For example, SRS 140 (e.g.,abnormality/emergency handler 340) may instruct local MEC workload andtraffic manager 350 to off-load the workload for affected sessions usingprocedures described above (e.g., transfer from MEC cluster 135-1 to MECcluster 135-2).

Process 900 may include handling traffic from the local MEC at theremote MEC clusters and monitoring resource usage (block 940),identifying recovery (block 945), and returning traffic and workload tothe preferred MEC resources (block 950). For example, SRS 140 at eachremote MEC area 210 may manage re-routed traffic according to trafficpolicies until receiving an indication that the original MEC cluster hasrecovered from the emergency condition. Once the abnormality isresolved, a network administrator may update the status forabnormality/emergency handler 340 and local MEC workload and trafficmanager 350 for the current MEC cluster may return the traffic andworkload to the preferred MEC cluster (e.g., from MEC cluster 135-2 toMEC cluster 135-1).

As set forth in this description and illustrated by the drawings,reference is made to “an exemplary embodiment,” “an embodiment,”“embodiments,” etc., which may include a particular feature, structureor characteristic in connection with an embodiment(s). However, the useof the phrase or term “an embodiment,” “embodiments,” etc., in variousplaces in the specification does not necessarily refer to allembodiments described, nor does it necessarily refer to the sameembodiment, nor are separate or alternative embodiments necessarilymutually exclusive of other embodiment(s). The same applies to the term“implementation,” “implementations,” etc.

The foregoing description of embodiments provides illustration, but isnot intended to be exhaustive or to limit the embodiments to the preciseform disclosed. Accordingly, modifications to the embodiments describedherein may be possible. For example, various modifications and changesmay be made thereto, and additional embodiments may be implemented,without departing from the broader scope of the invention as set forthin the claims that follow. The description and drawings are accordinglyto be regarded as illustrative rather than restrictive.

The terms “a,” “an,” and “the” are intended to be interpreted to includeone or more items. Further, the phrase “based on” is intended to beinterpreted as “based, at least in part, on,” unless explicitly statedotherwise. The term “and/or” is intended to be interpreted to includeany and all combinations of one or more of the associated items. Theword “exemplary” is used herein to mean “serving as an example.” Anyembodiment or implementation described as “exemplary” is not necessarilyto be construed as preferred or advantageous over other embodiments orimplementations.

In addition, while series of blocks have been described with regard tothe processes illustrated in FIGS. 6, 8, and 9, the order of the blocksmay be modified according to other embodiments. Further, non-dependentblocks may be performed in parallel. Additionally, other processesdescribed in this description may be modified and/or non-dependentoperations may be performed in parallel.

Embodiments described herein may be implemented in many different formsof software executed by hardware. For example, a process or a functionmay be implemented as “logic,” a “component,” or an “element.” Thelogic, the component, or the element, may include, for example, hardware(e.g., processor 510, etc.), or a combination of hardware and software(e.g., software 520).

Embodiments have been described without reference to the specificsoftware code because the software code can be designed to implement theembodiments based on the description herein and commercially availablesoftware design environments and/or languages. For example, varioustypes of programming languages including, for example, a compiledlanguage, an interpreted language, a declarative language, or aprocedural language may be implemented.

Use of ordinal terms such as “first,” “second,” “third,” etc., in theclaims to modify a claim element does not by itself connote anypriority, precedence, or order of one claim element over another, thetemporal order in which acts of a method are performed, the temporalorder in which instructions executed by a device are performed, etc.,but are used merely as labels to distinguish one claim element having acertain name from another element having a same name (but for use of theordinal term) to distinguish the claim elements.

Additionally, embodiments described herein may be implemented as anon-transitory computer-readable storage medium that stores data and/orinformation, such as instructions, program code, a data structure, aprogram module, an application, a script, or other known or conventionalform suitable for use in a computing environment. The program code,instructions, application, etc., is readable and executable by aprocessor (e.g., processor 510) of a device. A non-transitory storagemedium includes one or more of the storage mediums described in relationto memory/storage 515.

To the extent the aforementioned embodiments collect, store or employpersonal information of individuals, it should be understood that suchinformation shall be collected, stored and used in accordance with allapplicable laws concerning protection of personal information.Additionally, the collection, storage and use of such information may besubject to consent of the individual to such activity, for example,through well known “opt-in” or “opt-out” processes as may be appropriatefor the situation and type of information. Storage and use of personalinformation may be in an appropriately secure manner reflective of thetype of information, for example, through various encryption andanonymization techniques for particularly sensitive information.

No element, act, or instruction set forth in this description should beconstrued as critical or essential to the embodiments described hereinunless explicitly indicated as such.

All structural and functional equivalents to the elements of the variousaspects set forth in this disclosure that are known or later come to beknown to those of ordinary skill in the art are expressly incorporatedherein by reference and are intended to be encompassed by the claims. Noclaim element of a claim is to be interpreted under 35 U.S.C. § 112(f)unless the claim element expressly includes the phrase “means for” or“step for.”

What is claimed is:
 1. A first network device in a first multi-accessedge computing (MEC) cluster, comprising: a processor configured to:store threshold values indicating overload conditions and priorityworkload rules for resource usage by multiple MEC clusters, wherein thepriority workload rules designate: designated priority levels fordifferent types of traffic, and different workload threshold values foreach of the designated priority levels; determine when a resource usageat the first MEC cluster reaches one of the workload threshold valuesfor a specified workload associated with the one of the workloadthreshold values; responsive to determining that the resource usagereaches one of the workload threshold values, identify a second MECcluster of the multiple MEC clusters; identify available resources inthe second MEC cluster, based on the priority workload rules; and basedon the identified available resources meeting requirements associatedwith the priority workload rules, redirect, based on the determining andidentifying, at least some of the resource usage associated with thespecified workload from the first MEC cluster to the second MEC cluster,wherein if the identified available resources do not meet therequirements associated with the priority workload rules, at least someof the resource usage from the first MEC cluster is redirected to adifferent MEC cluster of the multiple MEC clusters responsive to thedifferent MEC cluster meeting the requirements.
 2. The first networkdevice of claim 1, wherein the processor is further configured to:register, with the multiple MEC clusters, for participation in anintelligent MEC resource scheduling service; and send, to a secondnetwork device, a message indicating local resource usage levels oravailability at the first MEC cluster.
 3. The first network device ofclaim 1, wherein, when determining that the resource usage reaches oneof the workload threshold values, the processor is further configuredto: monitor current resource usage and predict future resource usage. 4.The first network device of claim 3, wherein, when predicting futureresource usage, the processor is further configured to: apply artificialintelligence algorithms to predict local future workloads for the firstMEC cluster.
 5. The first network device of claim 1, wherein, whenidentifying the available resources, the processor is further configuredto: receive a periodic resource usage message from a second networkdevice.
 6. The first network device of claim 5, wherein the periodicresource usage message indicates a minimum available bandwidth, amaximum latency value, or a length of available time for resources atthe second MEC cluster.
 7. The first network device of claim 1, wherein,when identifying the available resources, the processor is furtherconfigured to: verify an ability of the second MEC cluster to meetquality of service (QoS) requirements for an application instance. 8.The first network device of claim 7, wherein the verifying includesidentifying a lowest possible latency between the second MEC cluster andan end device requesting MEC resources.
 9. The first network device ofclaim 1, wherein, when identifying the available resources, theprocessor is further configured to: compare latency times of the secondMEC cluster with other latency times for other MEC clusters of themultiple MEC clusters.
 10. A method, comprising: storing, in a memory ofa first network device of a first multi-access edge computing (MEC)cluster of multiple MEC clusters, threshold values indicating overloadconditions and priority workload rules for resource usage by themultiple MEC clusters, wherein the priority workload rules designatedesignated priority levels for different types of traffic and differentworkload threshold values for each of the designated priority levels;determining, by the first network device, when a resource usage at thefirst MEC cluster reaches one of the threshold values for a specifiedworkload associated with the one of the workload threshold values;responsive to determining that the resource usage reaches one of theworkload threshold values, identifying a second MEC cluster of themultiple MEC clusters; identifying, by the first network device,available resources in the second MEC cluster, based on the priorityworkload rules; and based on the identified available resources meetingrequirements associated with the priority workload rules, redirect, bythe first network device and based on the determining and identifying,at least some of the resource usage associated with the specifiedworkload from the first MEC cluster to the second MEC cluster, whereinif the identified available resources do not meet the requirementsassociated with the priority workload rules, at least some of theresource usage from the first MEC cluster is redirected to a differentMEC cluster of the multiple MEC clusters responsive to the different MECcluster meeting the requirements.
 11. The method of claim 10, furthercomprising: registering, with the multiple MEC clusters, forparticipation in an intelligent MEC resource scheduling service; andsending, to a second network device, a message indicating local resourceusage levels or availability at the first MEC cluster.
 12. The method ofclaim 10, wherein determining that the resource usage reaches one of theworkload threshold values further comprises: monitoring current resourceusage and predicting future resource usage by the first MEC cluster. 13.The method of claim 12, wherein predicting future resource usage furthercomprises: applying artificial intelligence algorithms to predict localfuture workloads for the first MEC cluster.
 14. The method of claim 10,wherein identifying the available resources further comprises: sending,to the second MEC cluster, a request for resource availability; andreceiving, from the second MEC cluster, a resource usage messageresponsive to the request for resource availability.
 15. The method ofclaim 14, wherein the resource usage message indicates a minimumavailable bandwidth, a maximum latency value, and a length of availabletime for resources at the second MEC cluster.
 16. The method of claim10, wherein identifying the available resources further comprises:verifying an ability of the second MEC cluster to meet quality ofservice (QoS) requirements for an application instance.
 17. The methodof claim 10, wherein the identifying includes identifying a lowestlatency expected between the second MEC cluster and an end devicerequesting MEC resources.
 18. A non-transitory, computer-readablestorage media storing instructions executable by one or more processorsof one or more devices, which when executed cause the one or moredevices to: store threshold values indicating overload conditions andpriority workload rules for resource usage by a first multi-access edgecomputing (MEC) cluster of multiple MEC clusters, wherein the priorityworkload rules designate designated priority levels for different typesof traffic and different workload threshold values for each of thedesignated priority levels; determine when a resource usage at the firstMEC cluster reaches one of the threshold values for a specified workloadassociated with the one of the workload threshold values; responsive todetermining that the resource usage reaches one of the workloadthreshold values, identify a second MEC cluster of the multiple MECclusters; identify available resources in the second MEC cluster, basedon the priority workload rules; and based on the identified availableresources meeting requirements associated with the priority workloadrules, redirect, based on the determining and identifying, at least someof the resource usage associated with the specified workload from thefirst MEC cluster to the second MEC cluster, wherein if the identifiedavailable resources do not meet the requirements associated with thepriority workload rules, at least some of the resource usage from thefirst MEC cluster is redirected to a different MEC cluster of themultiple MEC clusters responsive to the different MEC cluster meetingthe requirements.
 19. The non-transitory, computer-readable storagemedia of claim 18, further comprising instructions to: detect, in thefirst MEC cluster, an abnormal condition; automatically collect, inresponse to the detection, additional data related to the abnormalcondition for a period of time; and allocate workload from the first MECcluster to another MEC cluster of the multiple MEC clusters.
 20. Thenon-transitory, computer-readable storage media of claim 18, wherein theinstructions to determine when the resource usage reaches one of theworkload threshold values, further comprise instructions to: monitorcurrent resource usage and predict future resource usage.